Key Capabilities
- Trace every interaction - Monitor model calls, retrievals, and tool executions with full context
- Track data lineage - See which vector database documents influenced each output
- Manage costs - Monitor token usage and API costs across all models
- Debug effectively - Get complete context for errors including inputs, outputs, and timing
- Collaborate with teams - Create trace collections for review and feedback
How It Works
Instrument your application
Add the Arcbeam connector to your Python or JavaScript code with a few lines.
What You Can Track
LLM Calls
Monitor all model interactions with token counts, costs, latency, and errors
Data Retrieval
See which documents are retrieved from vector databases and how they’re used
Agent Workflows
Visualize multi-step agent executions with full tool call history
Costs & Performance
Track spending across models and identify performance bottlenecks
Common Use Cases
Debug RAG Pipelines
Debug RAG Pipelines
Trace wrong answers back to the exact documents that were retrieved. See why your RAG system chose certain content and understand retrieval quality issues.
Optimize Costs
Optimize Costs
Identify expensive traces and compare costs across different models. Track token usage patterns and find opportunities to reduce spending without sacrificing quality.
Monitor Production
Monitor Production
Track error rates, response times, and usage across environments. Set up alerts for anomalies and maintain SLAs with real-time visibility.
Improve Data Quality
Improve Data Quality
Discover which documents are valuable and which go unused. Use data lineage to optimize your vector database and improve retrieval relevance.
Collaborate on Improvements
Collaborate on Improvements
Create trace collections for team review and feedback. Share specific examples of issues or successes with stakeholders.
Next Steps
Quickstart
Get Arcbeam running in 5 minutes
Send Traces
Instrument your application
Connect Data
Link your vector databases
View Traces
Start analyzing your data
